The impact of changing nicotine replacement therapy licensing laws in the United Kingdom: findings from the International Tobacco Control Four Country Survey
Bibliographic record
Abstract
AIM: To evaluate the impact of a new licence for some nicotine replacement therapy products (NRT) for cutting down to stop (CDTS) on changes in the pattern of NRT use. DESIGN: Quasi-experimental design comparing changes in NRT use across two waves of a population-based, replenished-panel, telephone survey conducted before and after the introduction of new licensing laws in the United Kingdom with changes in NRT use in three comparison countries (Australia, Canada and United States) without a licensing change. PARTICIPANTS: A total of 7386 and 7013 smokers and recent ex-smokers participating in the 2004 and/or 2006/7 survey. MEASUREMENTS: Data were collected on demographic and smoking characteristics as well as NRT use and access. In order to account for interdependence resulting from some participants being present in both waves, generalized estimation equations with an exchangeable correlation matrix were used to assess within-country changes and linear and logistic regressions to assess between-country differences in adjusted analyses. FINDINGS: NRT use was more prevalent in the United Kingdom and increased across waves in all countries but no wave x country interaction was observed. There was no evidence that the licensing change increased the prevalence of CDTS or the use of NRT (irrespective of how it was accessed) for CDTS in the United Kingdom relative to comparison countries. There was also no evidence for a change in concurrent smoking and NRT use among smokers not attempting to stop in the United Kingdom relative to comparison countries. CONCLUSION: The addition of the CDTS licence for some NRT products in the United Kingdom appears to have had very limited, if any, impact on NRT use in the first year after the licence change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".